Home Teaching Research Publications People Blackboard Software Contact Bachelor Study
Bioinformatics Master Study
Bioinformatics

Bodenhofer

Ulrich Bodenhofer

phone: +43-732-2468-4526
fax: +43-732-2468-4539

e-mail: [email protected]

Room: S3 309 (Computer Science Building, Science Park 3)

private homepage: ulrich.bodenhofer.com

Personal data/education

Employment record

Research topics

Machine learning in bioinformatics, with special focus on:

Publications

The following list only includes journal articles and contributions to edited books and peer-reviewed conferences since 2006. For a complete list of publications, see my private homepage.

2018

[2] V. Steinwandter, M. Šišmiš, P. Sagmeister, U. Bodenhofer, and C. Herwig. Multivariate analytics of chromatographic data: Visual computing based on moving window factor models. J. Chromatogr. B., 1092:179--190, 2018. [ bib | DOI ]
[1] S. Fischer, C. M. Freuling, T. Müller, F. Pfaff, U. Bodenhofer, D. Höper, M. Fischer, D. A. Marston, A. R. Fooks, T. C. Mettenleitner, F. J. Conraths, and T. Homeier-Bachmann. Defining objective clusters for rabies virus sequences using affinity propagation clustering. PLoS Neglect. Trop. Dis., 12(1):e0006182, 2018. [ bib | DOI ]

2017

[4] V. Greiff, C. R. Weber, J. Palme, U. Bodenhofer, E. Miho, U. Menzel, and S. T. Reddy. Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires. J. Immunol., 199(8):2985--2997, 2017. [ bib | DOI ]
[3] E. P. Klement, P. Bauer, U. Bodenhofer, M. Mittendorfer-Holzer, R. Pollak, R. Richter, and H. Exner. PapaGeno --- vollautomatische menschenähnliche Qualitätskontrolle für Aufdrucke auf Compact Discs. In M. Wirth, A. Reichl, and M. Gräser, editors, 50 Jahre Johannes Kepler Universität Linz. Innovationsfelder in Forschung, Lehre und universitärem Alltag, pages 265--282. Böhlau, Vienna, Austria, 2017. [ bib ]
[2] U. Bodenhofer, B. Haslinger-Eisterer, A. Minichmayer, G. Hermanutz, and J. Meier. Machine learning-based risk profile classification: A case study for heart valve surgery. In NIPS Workshop on Machine Learning for Health, Long Beach, CA, December 2017. [ bib ]
[1] U. Bodenhofer and S. Hochreiter. Position kernels as a key to making sense of very rare and private single-nucleotide variants. In NIPS Workshop on Machine Learning in Computational Biology, Long Beach, CA, December 2017. [ bib ]

2016

[4] A. Khaledi, M. Schniederjans, S. Pohl, R. Rainer, U. Bodenhofer, B. Xia, F. Klawonn, S. Bruchmann, M. Preusse, D. Eckweiler, A. Dötsch, and S. Häussler. Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa. Antimicrob. Agents Chemother., 60(8):4722--4733, 2016. [ bib | DOI ]
[3] N. Perualila-Tan, Z. Shkedy, W. Talloen, H. W. H. Göhlmann, The QSTAR Consortium, M. V. Moerbeke, and A. Kasim. Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery. J. Bioinform. Comput. Biol., 14:1650018, 2016. [ bib | DOI ]
[2] N. Perualila-Tan, A. Kasim, W. Talloen, B. Verbist, H. W. H. Göhlmann, The QSTAR Consortium, and Z. Shkedy. A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development. Stat. Appl. Genet. Mol. Biol., 15(4):291--304, 2016. [ bib | DOI ]
[1] F. Zehentmayr, C. Hauser-Kronberger, B. Zellinger, F. Hlubek, C. Schuster, U. Bodenhofer, G. Fastner, H. Deutschmann, P. Steininger, R. Reitsamer, T. Fischer, and F. Sedlmayer. Hsa-miR-375 is a predictor of local control in early stage breast cancer. Clin. Epigenetics, 8:28, 2016. [ bib | DOI ]

2015

[6] U. Bodenhofer, E. Bonatesta, C. Horejš-Kainrath, and S. Hochreiter. msa: an R package for multiple sequence alignment. Bioinformatics, 31(24):3997--3999, 2015. [ bib | DOI ]
[5] B. M. P. Verbist, G. R. Verheyen, L. Vervoort, M. Crabbe, D. Beerens, C. Bosmans, S. Jaensch, S. Osselaer, W. Talloen, I. Van den Wyngaert, G. Van Hecke, D. Wuyts, The QSTAR Consortium, F. Van Goethem, and H. W. H. Göhlmann. Integrating high-dimensional transcriptomics and image analysis tools into early safety screening: proof of concept for a new early drug development strategy. Chem. Res. Toxicol., 28(10):1914--1925, 2015. [ bib | DOI ]
[4] J. Palme, S. Hochreiter, and U. Bodenhofer. KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics, 31(15):2574--2576, 2015. [ bib | DOI ]
[3] B. Verbist, G. Klambauer, L. Vervoort, W. Talloen, The QSTAR Consortium, Z. Shkedy, O. Thas, A. Bender, H. W. Göhlmann, and S. Hochreiter. Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project. Drug Discov. Today, 20(5):505--513, 2015. [ bib | DOI ]
[2] A. C. Ravindranath, N. Perualila-Tan, A. Kasim, G. Drakakis, S. Liggi, S. C. Brewerton, D. Mason, M. J. Bodkin, D. A. Evans, A. Bhagwat, W. Talloen, H. W. Göhlmann, Z. Shkedy, A. Bender, and The QSTAR Consortium. Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis. Mol. Biosyst., 11(1):86--96, 2015. [ bib | DOI ]
[1] L. Běhounek, U. Bodenhofer, P. Cintula, S. Saminger-Platz, and P. Sarkoci. Graded dominance and related graded properties of fuzzy connectives. Fuzzy Sets and Systems, 262:78--101, 2015. [ bib | DOI ]

2014

[1] A. M. Lipp, K. Juhasz, C. Paar, C. Ogris, P. Eckerstorfer, R. Thuenauer, J. Hesse, B. Nimmervoll, H. Stockinger, G. J. Schütz, U. Bodenhofer, Z. Balogi, and A. Sonnleitner. Lck mediates signal transmission from CD59 to the TCR/CD3 pathway in Jurkat T cells. PLoS ONE, 9(1):e85934, 2014. [ bib | DOI ]

2013

[1] U. Bodenhofer, M. Krone, and F. Klawonn. Testing noisy numerical data for monotonic association. Inform. Sci., 245:21--37, 2013. [ bib | DOI ]

2012

[2] K. Schwarzbauer, U. Bodenhofer, and S. Hochreiter. Genome-wide chromatin remodeling identified at GC-rich long nucleosome-free regions. PLoS ONE, 7(11):e47924, 2012. [ bib | DOI ]
[1] G. Klambauer, K. Schwarzbauer, A. Mayr, D.-A. Clevert, A. Mitterecker, U. Bodenhofer, and S. Hochreiter. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Res., 40(9):e69, 2012. [ bib | DOI ]

2011

[2] U. Bodenhofer, A. Kothmeier, and S. Hochreiter. APCluster: an R package for affinity propagation clustering. Bioinformatics, 27(17):2463--2464, 2011. [ bib | DOI ]
[1] C. C. Mahrenholz, I. G. Abfalter, U. Bodenhofer, R. Volkmer, and S. Hochreiter. Complex networks govern coiled coil oligomerization --- predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics, 10(5):M110.004994, 2011. [ bib | DOI ]

2010

[3] L. Běhounek, P. Cintula, U. Bodenhofer, S. Saminger-Platz, and P. Sarkoci. On a graded notion of t-norm and dominance. In Proc. 40th IEEE Int. Symp. on Multiple-Valued Logic, pages 73--78. IEEE Computer Society, 2010. [ bib | DOI ]
[2] S. Hochreiter, U. Bodenhofer, M. Heusel, A. Mayr, A. Mitterecker, A. Kasim, T. Khamiakova, S. Van Sanden, D. Lin, W. Talloen, L. Bijnens, H. W. H. Göhlmann, Z. Shkedy, and D.-A. Clevert. FABIA: factor analysis for bicluster acquisition. Bioinformatics, 26(12):1520--1527, 2010. [ bib | DOI ]
[1] M. Štěpnička, U. Bodenhofer, M. Daňková, and V. Novák. Continuity issues of the implicational interpretation of fuzzy rules. Fuzzy Sets and Systems, 161(14):1959--1972, 2010. [ bib | DOI ]

2009

[3] D. Soukup, U. Bodenhofer, M. Mittendorfer-Holzer, and K. Mayer. Semi-automatic identification of print layers from a sequence of sample images: a case study from banknote print inspection. Image Vision Comput., 27(8):989--998, 2009. [ bib | DOI ]
[2] U. Bodenhofer, K. Schwarzbauer, M. Ionescu, and S. Hochreiter. Modeling position specificity in sequence kernels by fuzzy equivalence relations. In J. P. Carvalho, D. Dubois, U. Kaymak, and J. M. C. Sousa, editors, Proc. Joint 13th IFSA World Congress and 6th EUSFLAT Conference, pages 1376--1381, Lisbon, July 2009. [ bib | .pdf ]
[1] U. Bodenhofer. A survey of applications of fuzzy orderings: from databases to statistics and machine learning. In Proc. 14 Recontres Francophones sur la Logique Floue et ses Applications, pages 3--9, Annecy, November 2009. [ bib | .pdf ]

2008

[6] U. Bodenhofer. Orderings of fuzzy sets based on fuzzy orderings. part II: generalizations. Mathware Soft Comput., 15(3):219--249, 2008. [ bib | http | .pdf ]
[5] U. Bodenhofer. Orderings of fuzzy sets based on fuzzy orderings. part I: the basic approach. Mathware Soft Comput., 15(2):201--218, 2008. [ bib | http | .pdf ]
[4] U. Bodenhofer and F. Klawonn. Robust rank correlation coefficients on the basis of fuzzy orderings: initial steps. Mathware Soft Comput., 15(1):5--20, 2008. [ bib | http | .pdf ]
[3] U. Bodenhofer and M. Demirci. Strict fuzzy orderings with a given context of similarity. Internat. J. Uncertain. Fuzziness Knowledge-Based Systems, 16(2):147--178, 2008. [ bib | DOI ]
[2] L. Běhounek, U. Bodenhofer, and P. Cintula. Relations in Fuzzy Class Theory: Initial steps. Fuzzy Sets and Systems, 159(14):1729--1772, 2008. [ bib | DOI ]
[1] U. Bodenhofer. Lexicographic composition of similarity-based fuzzy orderings. In B. Bouchon-Meunier, C. Marsala, M. Rifqi, and R. R. Yager, editors, Uncertainty and Intelligent Information Systems, chapter 33, pages 457--469. World-Scientific, 2008. [ bib | .pdf ]

2007

[4] U. Bodenhofer, B. De Baets, and J. Fodor. A compendium of fuzzy weak orders: Representations and constructions. Fuzzy Sets and Systems, 158(8):811--829, 2007. [ bib | DOI ]
[3] U. Bodenhofer, M. Daňková, M. Štěpnička, and V. Novák. A plea for the usefulness of the deductive interpretation of fuzzy rules in engineering applications. In Proc. 16th IEEE Int. Conf. on Fuzzy Systems, pages 1567--1572, London, July 2007. [ bib | DOI ]
[2] U. Bodenhofer and F. Klawonn. Towards robust rank correlation measures for numerical observations on the basis of fuzzy orderings. In M. Štěpnička, V. Novák, and U. Bodenhofer, editors, Proc. 5th Conference of the European Society for Fuzzy Logic and Technology, volume I, pages 321--327, Ostrava, September 2007. [ bib | .pdf ]
[1] L. Běhounek, U. Bodenhofer, and P. Cintula. Valverde-style representation results in a graded framework. In M. Štěpnička, V. Novák, and U. Bodenhofer, editors, Proc. 5th Conference of the European Society for Fuzzy Logic and Technology, volume I, pages 153--160, Ostrava, September 2007. [ bib | .pdf ]

2006

[7] U. Bodenhofer, B. De Baets, and J. Fodor. General representation theorems for fuzzy weak orders. In H. C. M. de Swart, E. Orlowska, M. Roubens, and G. Schmidt, editors, Theory and Applications of Relational Structures as Knowledge Instruments II, volume 4342 of Lecture Notes in Artificial Intelligence, pages 229--244. Springer, Berlin, 2006. [ bib | DOI ]
[6] U. Bodenhofer, J. Küng, and S. Saminger. Flexible query answering using distance-based fuzzy relations. In H. C. M. de Swart, E. Orlowska, M. Roubens, and G. Schmidt, editors, Theory and Applications of Relational Structures as Knowledge Instruments II, volume 4342 of Lecture Notes in Artificial Intelligence, pages 207--228. Springer, Berlin, 2006. [ bib | DOI ]
[5] S. Saminger, U. Bodenhofer, E. P. Klement, and R. Mesiar. Aggregration of fuzzy relations and preservation of transitivity. In H. C. M. de Swart, E. Orlowska, M. Roubens, and G. Schmidt, editors, Theory and Applications of Relational Structures as Knowledge Instruments II, volume 4342 of Lecture Notes in Artificial Intelligence, pages 185--206. Springer, Berlin, 2006. [ bib | DOI ]
[4] B. Moser and U. Bodenhofer. Correspondences between fuzzy equivalence relations and kernels: theoretical results and potential applications. In Proc. 15th IEEE Int. Conf. on Fuzzy Systems, pages 10217--10223, Vancouver, BC, July 2006. [ bib | DOI ]
[3] R. Bergmair and U. Bodenhofer. Syntax-driven analysis of context-free languages with respect to fuzzy relational semantics. In Proc. 15th IEEE Int. Conf. on Fuzzy Systems, pages 9647--9654, Vancouver, BC, July 2006. [ bib | DOI ]
[2] U. Bodenhofer. Lexicographic composition of fuzzy orderings. In Proc. 11th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, volume 3, pages 2430--2437, Paris, July 2006. [ bib | .pdf ]
[1] E. Lughofer and U. Bodenhofer. Incremental learning of fuzzy basis function networks with a modified version of vector quantization. In Proc. 11th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, volume 1, pages 56--63, Paris, July 2006. [ bib | .pdf ]